Roy M. Turner

Context-mediated behavior for intelligent agents

Humans and other animals are exquisitely attuned to their context. Context affects almost all aspects of behavior, and it does so for the most part automatically, without a conscious reasoning effort. This would be a very useful property for an artificial agent to have: upon recognizing its context, the agents behavior would automatically adjust to fit it. This paper describescontext-mediated behavior(CMB), an approach to context-sensitive behavior we have developed over the past few years for intelligent autonomous agents. In CMB, contexts are represented explicitly ascontextual schemas(c-schemas). An agent recognizes its context by finding the c-schemas that match it, then it merges these to form a coherent representation of the current context. This includes not only a description of the context, but also information about how to behave in it. From that point until the next context change, knowledge for context-sensitive behavior is available with no additional effort. This is used to influence perception, make predictions about the world, handle unanticipated events, determine the context-dependent meaning of concepts, focus attention and select actions. CMB is being implemented in the Orca program, an intelligent controller for autonomous underwater vehicles.

Autonomous underwater vehicles: Current activities and research opportunities

Abstract Although the ocean covers over two thirds of our planet, it remains in poorly explored and only vaguely understood realm. The tools which currently exist to explore this hostile domain are limited. Autonomous Undersea Vehicle (AUV) systems are providing a new alternative for exploring and exploiting the undersea environment. This paper seeks to provide some understanding of the current state of these underwater robots and to highlight some of the research opportunities associated with underwater robotics. It suggests that the economic potential of the ocean, its strategic importance and its role in many of the global processes which mankind seeks to understand require increased research and development in the area of underwater robots. Although the last few years have seen increased attention focused in this area, much is left to be done.

A Model of Explicit Context Representation and Use for Intelligent Agents

Explicit representation of context and contextual knowledge is critical to intelligent agents. In this paper, we discuss our view of context and context-sensitive reasoning, based on several years of work on representing and using contextual knowledge. We describe our approach to context-sensitive reasoning, called context-mediated behavior (CMB), and discuss our experience related to reasoning in context in AI programs and our ongoing and future work in the area.

Organization and reorganization of autonomous oceanographic sampling networks

Systems such as autonomous oceanographic sampling networks (AOSNs) that have multiple autonomous or semi-autonomous components must have an organization which specifies the interactions between the components to allow them to distribute and accomplish the systems tasks. AOSNs present a special challenge. They will be deployed for long periods of time, and they are open systems whose composition will change over time. Such systems require the ability to autonomously organize and reorganize in response to changes in its composition, the environment, or the mission. In this paper, we present preliminary results from a project whose goal is to develop mechanisms to allow AOSNs to self-organize and reorganize. We discuss characteristics of AOSNs which impact their organization and give an overview of an approach which addresses their special requirements. We discuss a simulation methodology designed to simulate the aggregate properties of the protocols developed, and we describe preliminary results obtained using that simulator.

Using schemas for diagnosis

Medical diagnosis can be considered a planning task: actions are decided on, then executed. However, unlike traditional planning tasks, new information can arise any time, changing the situation and invalidating the plan. In order to cope with this, the reasoner must be able to interleave planning with plan execution. Our approach to this problem is called schema-based reasoning. In this approach, the reasoners procedural knowledge is represented as schemas, small packets of procedural knowledge which can be used to achieve a goal. When a new goal arises, the reasoner uses the goal and the features of the current consultation to retrieve an appropriate schema from memory; the schema is then applied to achieve the goal. If several schemas are active, the reasoner chooses between them using information about the current situation and information from strategic schemas. Our approach is implemented in MEDIC, a schema-based diagnostic reasoner whose domain is pulmonology.

Intelligent control of autonomous underwater vehicles: the Orca project

Establishing a useful presence in the ocean is becoming increasingly important to science, industry, and the military, yet the undersea environment is hostile to human presence. Autonomous underwater vehicles (AUVs) offer a solution. Before widespread use of AUVs is practical, however, mechanisms for intelligent control must be developed. In this paper, we report on the Orca project, which has the aim of creating a robust, intelligent, mission-level controller for long-range ocean science AUVs. Orca is now being built and tested in simulation; in the future, it will undergo in-water tests aboard the Marine Systems Engineering Laboratorys EAVE-III vehicles. In this paper, we discuss the motivation behind the project, the Orca program, and our current status and future work.

Retaining majors through the introductory sequence

Retention is an important issue for Computer Science Departments. In many cases students leave the major due to frustrations with programming in the complex languages often used in CS1 and CS2 or because they do not understand that computer science is much more than programming. We have redesigned our introductory sequence to include a rigorous, non-programming introduction to the field and a CS1 course which uses Scheme so that students can focus on the principles of programming instead of the complexities of a particular language. In the first year that we have required these courses in our major, we have had positive results. In this paper, we describe what we have learned through discussions with students and student surveys.

Teaching entering students to think like computer scientists

This paper describes a new course developed at University of Maine to help students better understand the discipline of computer science and to aid us in recruiting and retaining majors. The course verview of computer science, but also, through focusing on particular topics at an advanced level, begins to teach students how computer scientists think about problems. The course has been taught in Fall 2002, 2003 and 2004. This paper describes the course and discusses our results from the first two years.

Using Explicit, A Priori Contextual Knowledge in an Intelligent Web Search Agent

The development of intelligent Web search agents will become increasingly important as the amount of information on the Web continues to grow. Intelligently searching the Web depends on the searcher understanding not only the context of the query, including the person for whom the search is being done, but also the context of the results, including the information sources and the retrieved information itself. Consequently, intelligent Web search agents will need to have mechanisms for representing and using contextual knowledge. In this paper, we discuss the kinds of contexts and contextual knowledge such an agent will encounter. We use as an example a Web search agent we are beginning to develop, ferret, that will search for scholarly information about music. We then propose some ways in which explicitly represented, a priori contextual knowledge can be used by the search agent, and we discuss directions for future research.

Context-Mediated Behavior

Context-mediated behavior (CMB) is an approach to giving intelligent agents the ability to recognize their context at all times and to behave appropriately for it. It is based on the idea that contexts—classes of situations—should be represented explicitly as first-class objects. These representations (contextual schemas) are then retrieved based on a diagnostic process of context assessment. Contextual schemas contain descriptive knowledge about the context, including predicted features and context-dependent meaning of concepts. They also include prescriptive features that tell the agent how to behave in the context. This approach has been implemented in several systems, including an intelligent controller for autonomous underwater vehicles (AUVs), and the author is now exploring distributing the process in multiagent systems.